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Abstract

It is likely in real-world applications that only little data is available for training a knowledge-based system. We present a method for automatically training the knowledge-representing membership functions of a Fuzzy-Pattern-Classification system that works also when only little data is available and the universal set is described insufficiently. Actually, this paper presents how the Modified-Fuzzy-Pattern-Classifier’s membership functions are trained using probability distribution functions.

Keywords

Fuzzy Logic Probability Theory Fuzzy-Pattern-Classification Machine Learning Artificial Intelligence Pattern Recognition 

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References

  1. 1.
    Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems 1, 3–28 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Bocklisch, S.F.: Prozeßanalyse mit unscharfen Verfahren. Verlag Technik, Berlin (1987)Google Scholar
  3. 3.
    Lohweg, V.: Ein Beitrag zur effektiven Implementierung adaptiver Spektraltransformationen in applikationsspezifische integrierte Schaltkreise. PhD thesis, Technische Universität Chemnitz (2003)Google Scholar
  4. 4.
    Lohweg, V., Diederichs, C., Müller, D.: Algorithms for hardware-based pattern recognition. EURASIP Journal on Applied Signal Processing 2004(12), 1912–1920 (2004)CrossRefGoogle Scholar
  5. 5.
    Niederhöfer, M., Lohweg, V.: Application-based approach for automatic texture defect recognition on synthetic surfaces. In: IEEE International Conference on Emerging Technologies and Factory Automation, vol. (19), pp. 229–232 (2008)Google Scholar
  6. 6.
    Mönks, U., Lohweg, V., Larsen, H.L.: Aggregation Operator Based Fuzzy Pattern Classifier Design. In: Lohweg, V., Niggemann, O. (eds.) Lemgo Series on Industrial Information Technology, vol. 3. IT – Institut Industrial IT, Lemgo (2009)Google Scholar
  7. 7.
    Lohweg, V., Li, R., Türke, T., Willeke, H., Schaede, J.: FPGA-based Multi-sensor Real time Machine Vision for Banknote Printing. In: 21st annual Symposium on IS&T/SPIE Electronc Imaging, San Jose, California USA (2009)Google Scholar
  8. 8.
    Rodner, E., Denzler, J.: Learning with Few Examples by Transferring Feature Relevance. In: Denzler, J., Notni, G., Süße, H. (eds.) DAGM 2009. LNCS, vol. 5748, pp. 252–261. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Drobics, M., Bodenhofer, U., Peter Klement, E.: FS-FOIL: an inductive learning method for extracting interpretable fuzzy descriptions. International Journal of Approximate Reasoning 32(2-3), 131–152 (2003)CrossRefzbMATHGoogle Scholar
  10. 10.
    Eichhorn, K.: Entwurf und Anwendung von ASICs für musterbasierte Fuzzy-Klassifikationsverfahren am Beispiel eines Schwingungsüberwachungssystems. PhD thesis, Technische Universität Chemnitz (2000)Google Scholar
  11. 11.
    Salicone, S.: Measurement Uncertainty: An Approach via the Mathematical Theory of Evidence. Springer Series in Reliability Engineering. Springer Science+Business Media LLC, Boston (2007)zbMATHGoogle Scholar
  12. 12.
    Polyanin, A.D., Manzhirov, A.V.: Handbook of mathematics for engineers and scienctists. Chapman & Hall/CRC, Boca Raton (2007)zbMATHGoogle Scholar
  13. 13.
    Dujmović, J.J., Larsen, H.L.: Generalized conjunction/disjunction. International Journal of Approximate Reasoning 46(3), 423–446 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man and Cybernetics 18(1), 183–190 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Larsen, H.L.: Efficient Andness-Directed Importance Weighted Averaging Operators. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11(suppl. 1), 67–82 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Uwe Mönks
    • 1
  • Denis Petker
    • 2
  • Volker Lohweg
    • 1
  1. 1.inIT – Institute Industrial ITOstwestfalen-Lippe University of Applied SciencesLemgoGermany
  2. 2.OWITA GmbHLemgoGermany

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